Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 03012 | |
| Number of page(s) | 8 | |
| Section | Large Language Models, Generative AI, and Multimodal Learning | |
| DOI | https://doi.org/10.1051/itmconf/20268403012 | |
| Published online | 06 April 2026 | |
- J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, ... & D. Zhou, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, in Proceedings of Advances in Neural Information Processing Systems, NeurIPS, vol. 35, pp. 24824–24837 (2022) [Google Scholar]
- J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, D. Zhou, Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters, arXiv preprint arXiv:2212.10001 (2022) [Google Scholar]
- Z. Li, H. Liu, D. Zhou, T. Ma, Chain of Thought Empowers Transformers to Solve Inherently Serial Problems, arXiv preprint arXiv:2402.12875 (2024) [Google Scholar]
- X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, ... & D. Zhou, Self-Consistency Improves Chain of Thought Reasoning in Language Models, arXiv preprint arXiv:2203.11171 (2022) [Google Scholar]
- T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, Y. Iwasawa, Large Language Models are Zero-Shot Reasoners, in Proceedings of Advances in Neural Information Processing Systems, NeurIPS, vol. 35, pp. 22199–22213 (2022) [Google Scholar]
- M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, J. Sohl-Dickstein, Show Your Work: Scratchpads for Intermediate Computation with Language Models, arXiv preprint arXiv:2112.00114 (2021) [Google Scholar]
- Y. Deng, K. Prasad, R. Fernandez, P. Smolensky, V. Chaudhary, S. Shieber, Implicit Chain of Thought Reasoning via Knowledge Distillation, arXiv preprint arXiv:2311.01460 (2023) [Google Scholar]
- J.-F. Ton, M. F. Taufiq, Y. Liu, Understanding Chain-of-Thought in LLMs through Information Theory, arXiv preprint arXiv:2411.11984 (2024) [Google Scholar]
- K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, J. Schulman, Training Verifiers to Solve Math Word Problems, arXiv preprint arXiv:2110.14168 (2021) [Google Scholar]
- A. Talmor, J. Herzig, N. Lourie, J. Berant, CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge, in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL, pp. 4149–4158 (2019) [Google Scholar]
- J. Xu, H. Fei, L. Pan, Q. Liu, M.-L. Lee, W. Hsu, Faithful Logical Reasoning via Symbolic Chain-of-Thought, arXiv preprint arXiv:2405.18357 (2024) [Google Scholar]
- C.-Y. Hsieh, C. L. Li, C. K. Yeh, H. Nakhost, Y. Fujii, A. Ratner, ... & T. Pfister, Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes, in Findings of the Association for Computational Linguistics: ACL 2023, pp. 8003–8017 (2023) [Google Scholar]
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